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Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

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Details

Original languageEnglish
Pages (from-to)1216-1220
Number of pages5
JournalIEEE Signal Processing Letters
Volume25
Issue number8
DOIs
Publication statusPublished - 1 Aug 2018
Publication typeA1 Journal article-refereed

Abstract

We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF), exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease at every iteration of our framework, while their output is regularized by a nonlocal prior implicit within the NLF. Unlike complicated neural networks that embed the nonlocality prior within the layers of the network, our framework is modular, and it uses standard pretrained CNNs together with standard nonlocal filters. An instance of the proposed framework, called NN3D, is evaluated over large grayscale image datasets showing state-of-the-art performance.

Keywords

  • BM3D, convolutional neural network, image denoising, nonlocal filters

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